1. Quick tour of R
2. Tidyverse
3. ggplot2

The slides and worksheets are at:

https://github.com/hardin47/summerREU2019R

• Notebook structure were text, code, output are combined
• Benefit that R Markdown files are all text (easy to port)
• Uses Markdown language to format
• Plays nicely with RStudio and GitHub

An R Markdown chunk looks like this:

```{r}
  1 + 1
```

But typically, you will want text surrounding your work.

Here I've done some arithmetic as part of my analysis:
```{r}
  1 + 1
```

require(dplyr); require(tidyr)
require(ggplot2); require(lattice)
require(googlesheets)
require(babynames); require(nycflights13); require(NHANES)
Babynames <- babynames
names(NHANES) <- tolower(names(NHANES))

• rows (cases/observational units) and

• columns (variables).

• The key is that every row is a case and every column is a variable.

• No exceptions.

The pipe syntax (%>%) takes a data frame (or data table) and sends it to the argument of a function.

• x %>% f(y) is the same as f(x, y)

• y %>% f(x, ., z) is the same as f(x,y,z)

Data verbs take data tables as input and give data tables as output

1. select(): keeps variables of interest (and rename() ), i.e., columns
2. filter(): subsets cases of interest, i.e., rows
3. mutate(): transforms the variable (and transmute() like mutate, returns only new variables)
4. arrange(): reorders the cases
5. summarize(): computes summary statistics

• distinct(): returns the unique values in a table
• sample_n(): take a random row(s)
• head(): grab the first few rows
• tail(): grab the last few rows
• group_by(): SUCCESSIVE functions are applied to groups
• summarise():
  • min(), max(), mean(), sum(), sd(), median(), and IQR()
  • n(): number of observations in the current group
  • n_distinct(): number of unique values
  • first_value(), last_value(), and nth_value(x, n): (like x[1], x[length(x)], and x[n] )

Babynames %>% nrow()

## [1] 1924665

Babynames %>% names()

## [1] "year" "sex"  "name" "n"    "prop"

Babynames %>% glimpse()

## Observations: 1,924,665
## Variables: 5
## $ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880,…
## $ sex  <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F",…
## $ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret", …
## $ n    <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 1288,…
## $ prop <dbl> 0.07238359, 0.02667896, 0.02052149, 0.01986579, 0.01788843,…

Babynames %>% head()

## # A tibble: 6 x 5
##    year sex   name          n   prop
##   <dbl> <chr> <chr>     <int>  <dbl>
## 1  1880 F     Mary       7065 0.0724
## 2  1880 F     Anna       2604 0.0267
## 3  1880 F     Emma       2003 0.0205
## 4  1880 F     Elizabeth  1939 0.0199
## 5  1880 F     Minnie     1746 0.0179
## 6  1880 F     Margaret   1578 0.0162

Babynames %>% sample_n(size=5)

## # A tibble: 5 x 5
##    year sex   name           n       prop
##   <dbl> <chr> <chr>      <int>      <dbl>
## 1  1994 F     Raylee        10 0.00000513
## 2  2008 M     Adeel          8 0.00000367
## 3  1987 M     Atif          13 0.00000667
## 4  1978 M     Deandra       16 0.00000936
## 5  2017 M     Reddington     9 0.00000458

names(NHANES)

##  [1] "id"               "surveyyr"         "gender"          
##  [4] "age"              "agedecade"        "agemonths"       
##  [7] "race1"            "race3"            "education"       
## [10] "maritalstatus"    "hhincome"         "hhincomemid"     
## [13] "poverty"          "homerooms"        "homeown"         
## [16] "work"             "weight"           "length"          
## [19] "headcirc"         "height"           "bmi"             
## [22] "bmicatunder20yrs" "bmi_who"          "pulse"           
## [25] "bpsysave"         "bpdiaave"         "bpsys1"          
## [28] "bpdia1"           "bpsys2"           "bpdia2"          
## [31] "bpsys3"           "bpdia3"           "testosterone"    
## [34] "directchol"       "totchol"          "urinevol1"       
## [37] "urineflow1"       "urinevol2"        "urineflow2"      
## [40] "diabetes"         "diabetesage"      "healthgen"       
## [43] "daysphyshlthbad"  "daysmenthlthbad"  "littleinterest"  
## [46] "depressed"        "npregnancies"     "nbabies"         
## [49] "age1stbaby"       "sleephrsnight"    "sleeptrouble"    
## [52] "physactive"       "physactivedays"   "tvhrsday"        
## [55] "comphrsday"       "tvhrsdaychild"    "comphrsdaychild" 
## [58] "alcohol12plusyr"  "alcoholday"       "alcoholyear"     
## [61] "smokenow"         "smoke100"         "smoke100n"       
## [64] "smokeage"         "marijuana"        "agefirstmarij"   
## [67] "regularmarij"     "ageregmarij"      "harddrugs"       
## [70] "sexever"          "sexage"           "sexnumpartnlife" 
## [73] "sexnumpartyear"   "samesex"          "sexorientation"  
## [76] "pregnantnow"

find the sleep variables

NHANESsleep <- NHANES %>% 
  select(gender, age, weight, race1, race3, education, sleeptrouble,
         sleephrsnight, tvhrsday, tvhrsdaychild, physactive)
names(NHANESsleep)

##  [1] "gender"        "age"           "weight"        "race1"        
##  [5] "race3"         "education"     "sleeptrouble"  "sleephrsnight"
##  [9] "tvhrsday"      "tvhrsdaychild" "physactive"

dim(NHANESsleep)

## [1] 10000    11

subset for college students

NHANESsleep <- NHANESsleep %>% filter(age %in% c(18:22))
histogram(~age, data=NHANESsleep)

mutate or transmute to create a new variable?

NHANESsleep %>% mutate(weightlb = weight*2.2) %>% head(3)

## # A tibble: 3 x 12
##   gender   age weight race1 race3 education sleeptrouble sleephrsnight
##   <fct>  <int>  <dbl> <fct> <fct> <fct>     <fct>                <int>
## 1 female    21  104.  Black <NA>  Some Col… Yes                      4
## 2 female    21  104.  Black <NA>  Some Col… Yes                      4
## 3 female    22   81.8 Black <NA>  Some Col… No                       5
## # … with 4 more variables: tvhrsday <fct>, tvhrsdaychild <int>,
## #   physactive <fct>, weightlb <dbl>

mutate or transmute to create a new variable?

NHANESsleep %>% transmute(weightlb = weight*2.2) %>% head(3)

## # A tibble: 3 x 1
##   weightlb
##      <dbl>
## 1     228.
## 2     228.
## 3     180.

mutate or transmute to create a new variable?

NHANESsleep <- NHANESsleep %>% mutate(weightlb = weight*2.2)

# number of people (cases) in NHANES
NHANES %>% summarise(n())

## # A tibble: 1 x 1
##   `n()`
##   <int>
## 1 10000

# total weight of all the people in NHANES (silly)
NHANES %>% 
  mutate(weightlb = weight*2.2) %>% 
  summarise(sum(weightlb, na.rm=TRUE))

## # A tibble: 1 x 1
##   `sum(weightlb, na.rm = TRUE)`
##                           <dbl>
## 1                      1549419.

# mean weight of all the people in NHANES
NHANES %>% 
  mutate(weightlb = weight*2.2) %>% 
  summarise(mean(weightlb, na.rm=TRUE))

## # A tibble: 1 x 1
##   `mean(weightlb, na.rm = TRUE)`
##                            <dbl>
## 1                           156.

# mean weight of all the people in NHANES
NHANES %>% 
  mutate(weightlb = weight*2.2) %>%  
  group_by(education) %>%  
  summarise(mean(weightlb, na.rm=TRUE))

## # A tibble: 6 x 2
##   education      `mean(weightlb, na.rm = TRUE)`
##   <fct>                                   <dbl>
## 1 8th Grade                               173. 
## 2 9 - 11th Grade                          181. 
## 3 High School                             183. 
## 4 Some College                            185. 
## 5 College Grad                            177. 
## 6 <NA>                                     91.7

# mean weight of all the people in NHANES
NHANES %>% mutate(weightlb = weight*2.2) %>% 
  group_by(education) %>% 
  summarise(avewt = mean(weightlb, na.rm=TRUE)) %>% 
  arrange(avewt)

## # A tibble: 6 x 2
##   education      avewt
##   <fct>          <dbl>
## 1 <NA>            91.7
## 2 8th Grade      173. 
## 3 College Grad   177. 
## 4 9 - 11th Grade 181. 
## 5 High School    183. 
## 6 Some College   185.

What I will try to do

• give a tour of ggplot2

• explain how to think about plots the ggplot2 way

• prepare/encourage you to learn more later

What I can't do in one session

• show every bell and whistle

• make you an expert at using ggplot2

require(mosaic)
require(lubridate) # package for working with dates
data(Births78)     # restore fresh version of Births78
head(Births78, 3)

##         date births wday year month day_of_year day_of_month day_of_week
## 1 1978-01-01   7701  Sun 1978     1           1            1           1
## 2 1978-01-02   7527  Mon 1978     1           2            2           2
## 3 1978-01-03   8825  Tue 1978     1           3            3           3

geom: the geometric "shape" used to display data (glyph)

• bar, point, line, ribbon, text, etc.

aesthetic: an attribute controlling how geom is displayed

• x position, y position, color, fill, shape, size, etc.

scale: conversion of raw data to visual display

• particular assignment of colors, shapes, sizes, etc.

guide: helps user convert visual data back into raw data (legends, axes)

stat: a transformation applied to data before geom gets it

• example: histograms work on binned data

Two Questions:

1. What do we want R to do? (What is the goal?)

2. What does R need to know?

Two Questions:

1. Goal: scatterplot = a plot with points

2. What does R need to know?

   • data source: Births78

   • aesthetics:

     • date -> x
     • births -> y
     • default color (same for all points)

1. Goal: scatterplot = a plot with points

   • ggplot() + geom_point()

2. What does R need to know?

   • data source: data = Births78

   • aesthetics: aes(x = date, y = births)

first option

ggplot(data = Births78, aes(x = date, y = births)) + 
  geom_point()

second option

ggplot() +
  geom_point(data = Births78, aes(x = date, y = births))  

What has changed?

• new aesthetic: mapping color to day of week

The wday() function in the lubridate package computes the day of the week from a date.

Births78 <-  
  Births78 %>% 
  mutate(wday = wday(date, label = TRUE))

ggplot(data = Births78) +
  geom_point(aes(x = date, y = births, color = wday))

This time we use lines instead of dots

ggplot(data = Births78) +
  geom_line(aes(x = date, y = births, color = wday)) 

This time we have two layers, one with points and one with lines

ggplot(data = Births78, 
       aes(x = date, y = births, color = wday)) + 
  geom_point() +  geom_line()

• The layers are placed one on top of the other: the points are below and the lines are above.

• data and aes specified in ggplot() affect all geoms

Births78 %>% 
  ggplot(aes(x = date, y = births, color = wday)) + 
  geom_point() + 
  geom_line()

Births78 %>%
  ggplot(aes(x = date, y = births, color = "navy")) + 
  geom_point()  

This is mapping the color aesthetic to a new variable with only one value ("navy").
So all the dots get set to the same color, but it's not navy.

If we want to set the color to be navy for all of the dots, we do it this way:

Births78 %>%
  ggplot(aes(x = date, y = births)) +   # map these 
  geom_point(color = "navy")        # set this

• Note that color = "navy" is now outside of the aesthetics list. That's how ggplot2 distinguishes between mapping and setting.

Births78 %>%
  ggplot(aes(x = date, y = births)) + 
  geom_line(aes(color = wday)) +       # map color here
  geom_point(color = "navy")           # set color here

• ggplot() establishes the default data and aesthetics for the geoms, but each geom may change these defaults.

• good practice: put into ggplot() the things that affect all (or most) of the layers; rest in geom_blah()

apropos("^geom_") %>% head(21)

 [1] "geom_abline"     "geom_area"       "geom_ash"       
 [4] "geom_bar"        "geom_barh"       "geom_bin2d"     
 [7] "geom_blank"      "geom_boxplot"    "geom_boxploth"  
[10] "geom_col"        "geom_colh"       "geom_contour"   
[13] "geom_count"      "geom_crossbar"   "geom_crossbarh" 
[16] "geom_curve"      "geom_density"    "geom_density_2d"
[19] "geom_density2d"  "geom_dotplot"    "geom_errorbar"  

help pages will tell you their aesthetics, default stats, etc.

?geom_area             # for example

Births78 %>%
  ggplot(aes(x = date, y = births, fill = wday)) + 
  geom_area()

This is not a good plot

• overplotting is hiding much of the data
• extending y-axis to 0 may or may not be desirable.

Most (all?) graphics are intended to help us make comparisons

• How does something change over time?
• Do my treatments matter? How much?
• Do men and women respond the same way?

Key plot metric: Does my plot make the comparisions I am interested in

• easily, and
• accurately?

HELPrct: Health Evaluation and Linkage to Primary care randomized clinical trial

?HELPrct

Subjects admitted for treatment for addiction to one of three substances.

HELPrct %>% 
  ggplot(aes(x = substance)) + 
  geom_bar()

• Hmm. What's up with y?

  • stat_bin() is being applied to the data before the geom_bar() gets to do its thing. Binning creates the y values.

\[ \mbox{org data} \stackrel{\mbox{\ stat\ }}{\longrightarrow} \mbox{statified} \stackrel{\mbox{aesthetics}}{\longrightarrow} \mbox{aesthetic data} \stackrel{\mbox{\ scales\ }}{\longrightarrow} \mbox{scaled data} \]

Simplifications:

• Aesthetics get computed twice, once before the stat and again after. Examples: bar charts, histograms

• We need to look at the aesthetics to figure out which variable to bin

  • then the stat does the binning
  • bin counts become part of the aesthetics for geom: y = ..count..

• This process happens in each layer

• stat_identity() is the "do nothing" stat.

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice the messages

• stat_bin: Histograms are not mapping the raw data but binned data.
  stat_bin() performs the data transformation.

• binwidth: a default binwidth has been selected, but we should really choose our own.

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_histogram(binwidth = 2)

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_freqpoly(binwidth = 2)

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_density()

Every geom comes with a default stat

• for simple cases, the stat is stat_identity() which does nothing
• we can mix and match geoms and stats however we like

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_line(stat = "density")

Every stat comes with a default geom, every geom with a default stat

• we can specify stat instead of geom, if we prefer
• we can mix and match geoms and stats however we like

HELPrct %>% 
  ggplot(aes(x = age)) + 
  stat_density( geom = "line")

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_point(stat = "bin", binwidth = 3) + 
  geom_line(stat = "bin", binwidth = 3)  

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_area(stat = "bin", binwidth = 3)  

HELPrct %>% 
  ggplot(aes(x = age)) + 
  geom_point(stat = "bin", binwidth = 3, aes(size = ..count..)) +
  geom_line(stat = "bin", binwidth = 3) 

Create a plot that shows the distribution of the average daily alcohol consumption in the past 30 days (i1).

HELPrct %>% 
  ggplot(aes(x = i1)) + geom_histogram()

HELPrct %>% 
  ggplot(aes(x = i1)) + geom_area(stat = "density")

Q. How does alcohol consumption (or age, your choice) differ by sex and substance (alcohol, cocaine, heroin)?

Decisions:

• How will we display the variables: i1 (or age), sex, substance

• What comparisons are we most interested in?

Give it a try.

• Note: I'm cheating a bit. You may want to do some things I haven't shown you yet. (Feel free to ask.)

Using color and linetype:

HELPrct %>% 
  ggplot(aes(x = i1, color = substance, linetype = sex)) + 
  geom_line(stat = "density")

Using color and facets

HELPrct %>% 
  ggplot(aes(x = i1, color = substance)) + 
  geom_line(stat = "density") + facet_grid( . ~ sex )

Boxplots use stat_quantile() which computes a five-number summary (roughly the five quartiles of the data) and uses them to define a "box" and "whiskers". The quantitative variable must be y, and there must be an additional x variable.

HELPrct %>% 
  ggplot(aes(x = substance, y = age, color = sex)) + 
  geom_boxplot()

Horizontal boxplots are obtained by flipping the coordinate system:

HELPrct %>% 
  ggplot(aes(x = substance, y = age, color = sex)) + 
  geom_boxplot() +
  coord_flip()

• coord_flip() may be used with other plots as well to reverse the roles of x and y on the plot.

We've triggered a new feature: dodge (for dodging things left/right). We can control how much if we set the dodge manually.

HELPrct %>% 
  ggplot(aes(x = substance, y = age, color = sex)) + 
  geom_boxplot(position = position_dodge(width = 1)) 

require(NHANES)
dim(NHANES)

## [1] 10000    76

NHANES %>%  ggplot(aes(x = height, y = weight)) +
  geom_point() + facet_grid( gender ~ pregnantnow )

• Although we can see a generally positive association (as we would expect), the overplotting may be hiding information.

One way to deal with overplotting is to set the opacity low.

NHANES %>% 
  ggplot(aes(x = height, y = weight)) +
  geom_point(alpha = 0.01) + facet_grid( gender ~ pregnantnow )

Alternatively (or simultaneously) we might prefere a different geom altogether.

NHANES %>% 
  ggplot(aes(x = height, y = weight)) +
  geom_density2d() + facet_grid( gender ~ pregnantnow )

ggplot( data = HELPrct, aes(x = sex, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_jitter(alpha = .6) +
  coord_flip()

ggplot( data = HELPrct, aes(x = sex, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_point(alpha = .6, position = position_jitter(width = .1, height = 0)) +
  coord_flip()

NHANES %>% 
  ggplot(aes(x = height, y = weight)) +
  geom_point() + facet_grid( gender ~ pregnantnow ) +
  labs(x = "waist (m)", y = "weight (kg)", 
       title = "weight vs height")

• scales (fine tuning mapping from data to plot)

• guides (so reader can map from plot to data)

• coords (coord_flip() is good to know about)

• themes (for customizing appearance)

require(ggthemes)
ggplot(data = Births78, aes(x = date, y = births)) +
       geom_point() + theme_wsj() # wall street journal

• scales (fine tuning mapping from data to plot)

• guides (so reader can map from plot to data)

• coords (coord_flip() is good to know about)

• themes (for customizing appearance)

require(xkcd)
ggplot(data = Births78, aes(x = date, y = births, colour = wday)) +
       geom_smooth(se = FALSE) + theme_xkcd()

• scales (fine tuning mapping from data to plot)

• guides (so reader can map from plot to data)

• coords (coord_flip() is good to know about)

• themes (for customizing appearance)

• position (position_dodge() can be used for side by side bars)

ggplot(data = HELPrct, 
       aes(x = substance, y = age, color = sex)) +
  geom_violin(position = position_dodge()) +
  geom_point(aes(color = sex, fill = sex), 
             position = position_jitterdodge()) 

• scales (fine tuning mapping from data to plot)

• guides (so reader can map from plot to data)

• themes (for customizing appearance)

• position (position_dodge(), position_jitterdodge(), position_stack(), etc.)

ggplot(data = HELPrct, 
       aes(x = substance, y = age, color = sex)) +
  geom_boxplot(position = position_dodge(width = 1)) +
  geom_point(aes(fill = sex), alpha = .5, 
    position = position_jitterdodge(dodge.width = 1)) + 
  facet_wrap(~homeless)

• docs.ggplot2.org/

• Winston Chang's: R Graphics Cookbook

• google whatevery you want plus ggplot2

ggvis

• dynamic graphics (brushing, sliders, tooltips, etc.)

• similar structure to ggplot2 but different syntax and names

Dynamic documents

• combination of RMarkdown, ggvis, and shiny

• GitHub is far and away the best method for collaborative and reproducible research.

• Small learning curve, but well worth it in the end.

• Highly recommend working with GitHub this summer while you have lots of time to try things and make mistakes

• Today is already too packed to learn GitHub, too

Keep practicing, keep learning

ImpostR Syndrome